Early-Career Researchers in China during the Pandemic: Qualitative Evidence from a Longitudinal Study
Bibliographic record
Abstract
The outbreak of COVID-19 has changed the world in many aspects, and global scientific research has also been challenged. Early-career researchers (ECRs) who just start academic careers are prominently affected by the pandemic. To explore how Chinese ECRs have been affected, longitudinal qualitative interviews were conducted with twenty-four Chinese ECRs from different disciplines and universities in 2020 and 2021. As the results show, Chinese ECRs worry that their future prospects are limited by increased workloads and reduced productivity. However, the new wave of change did not occur for them, and their work resumed its routine and was relatively stable. Over two years, Chinese ECRs got used to working from home and giving online courses, but they were increasingly confused about when the pandemic will end. What bothers ECRs most is the inefficiency of communication and collaboration due to the travel bans, although they became familiar with the online way.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".